A study on quality, architecture, and performance relationships in sonar classifiers trained with simulated data.

Authors

Thomas E Blanford, David P Williams

Published in

JASA express letters. Volume 6. Issue 10. Oct 01, 2026.

Abstract

Convolutional neural networks (CNNs) are commonly used to classify objects observed in synthetic aperture sonar imagery. Controlled laboratory data and complementary simulated imagery produced with physics-based acoustic scattering models were used to investigate the relationships between classification accuracy, simulation quality, and network architecture. Three experiments investigated simulation errors that arise from imperfect models, inaccurate knowledge of target shape, and inaccurate knowledge of target appearance due to partial burial. In a matched environment, results show CNN-based classifiers can be designed to be insensitive to simulation errors of a certain scale, allowing coarse, imperfect simulations for training.

PMID:
42831748
Bibliographic data and abstract were imported from PubMed on 05 Oct 2026.

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